Practice Applications In Adtech, Recommender Systems (9.9.5) - Reinforcement Learning and Bandits
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Applications in AdTech, Recommender Systems

Practice - Applications in AdTech, Recommender Systems

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does MAB stand for?

💡 Hint: Think about a slot machine with multiple options.

Question 2 Easy

Name a use case of MAB in AdTech.

💡 Hint: Consider how ads appear on websites.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of Multi-Armed Bandits in AdTech?

Maximize revenue
Minimize costs
Ensure equality
Maximize user engagement

💡 Hint: Think about what keeps users coming back.

Question 2

True or False: MAB is only useful for determining pricing strategies in AdTech.

True
False

💡 Hint: Consider the broader applications of this approach.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Imagine you run a digital marketing campaign with three ads showing vastly different engagement rates. Design a MAB-based strategy that lets you explore new ads while still pushing the current best performer.

💡 Hint: Consider the percentage splits for the exploration and exploitation phases.

Challenge 2 Hard

How can a recommender system use MAB to handle seasonal changes in user preferences, like the transition from summer to winter clothing?

💡 Hint: Think about the timing of user engagement with products.

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Reference links

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